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Transconjunctival Strategy Using Lateral Skin color Off shoot for Recouvrement of Orbit Zygomatic Complicated Cracks: Method Description as well as a Case Collection Examination.
Copyright © 2020 Carlton, Sullivan-Toole, Ghane and Richey.Stereotactic electroencephalogaphy (sEEG) utilizes localized, penetrating depth electrodes to measure electrophysiological brain activity. It is most commonly used in the identification of epileptogenic zones in cases of refractory epilepsy. The implanted electrodes generally provide a sparse sampling of a unique set of brain regions including deeper brain structures such as hippocampus, amygdala and insula that cannot be captured by superficial measurement modalities such as electrocorticography (ECoG). Despite the overlapping clinical application and recent progress in decoding of ECoG for Brain-Computer Interfaces (BCIs), sEEG has thus far received comparatively little attention for BCI decoding. Additionally, the success of the related deep-brain stimulation (DBS) implants bodes well for the potential for chronic sEEG applications. This article provides an overview of sEEG technology, BCI-related research, and prospective future directions of sEEG for long-term BCI applications. Copyright © 2020 Herff, Krusienski and Kubben.Several cues are used to convey musical emotion, the two primary being musical mode and musical tempo. Specifically, major and minor modes tend to be associated with positive and negative valence, respectively, and songs at fast tempi have been associated with more positive valence compared to songs at slow tempi (Balkwill and Thompson, 1999; Webster and Weir, 2005). In Experiment I, we examined the relative weighting of musical tempo and musical mode among adult cochlear implant (CI) users combining electric and contralateral acoustic stimulation, or "bimodal" hearing. Our primary hypothesis was that bimodal listeners would utilize both tempo and mode cues in their musical emotion judgments in a manner similar to normal-hearing listeners. Our secondary hypothesis was that low-frequency (LF) spectral resolution in the non-implanted ear, as quantified via psychophysical tuning curves (PTCs) at 262 and 440 Hz, would be significantly correlated with degree of bimodal benefit for musical emotion perception. In Experiment II, we investigated across-channel spectral resolution using a spectral modulation detection (SMD) task and neural representation of temporal fine structure via the frequency following response (FFR) for a 170-ms /da/ stimulus. Results indicate that CI-alone performance was driven almost exclusively by tempo cues, whereas bimodal listening demonstrated use of both tempo and mode. Additionally, bimodal benefit for musical emotion perception may be correlated with spectral resolution in the non-implanted ear via SMD, as well as neural representation of F0 amplitude via FFR - though further study with a larger sample size is warranted. Thus, contralateral acoustic hearing can offer significant benefit for musical emotion perception, and the degree of benefit may be dependent upon spectral resolution of the non-implanted ear. Copyright © 2020 D’Onofrio, Caldwell, Limb, Smith, Kessler and Gifford.Repetitive transcranial magnetic stimulation (rTMS) technology, which is amongst the most used non-invasive brain stimulation techniques currently available, has developed rapidly from 2009 to 2018. However, reports on the trends of rTMS using bibliometric analysis are rare. The goal of the present bibliometric analysis is to analyze and visualize the trends of rTMS, including general (publication patterns) and emerging trends (research frontiers), over the last 10 years by using the visual analytic tool CiteSpace V. Publications related to rTMS from 2009 to 2018 were retrieved from the Web of Science (WoS) database, including 2,986 peer-reviewed articles/reviews. Active authors, journals, institutions, and countries were identified by WoS and visualized by CiteSpace V, which could also detect burst changes to identify emerging trends. GraphPad Prism 8 was used to analyze the time trend of annual publication outputs. The USA ranked first in this field. Pascual-Leone A (author A), Fitzgerald PB (author B), George MS (author C), Lefaucheur JP (author D), and Fregni F (author E) made great contributions to this field of study. The most prolific institution to publish rTMS-related publications in the last decade was the University of Toronto. The journal Brain Stimulation published most papers. Lefaucheur et al.'s paper in 2014, and the keyword "sham controlled trial" showed the strongest citation bursts by the end of 2018, which indicates increased attention to the underlying work, thereby indicating the research frontiers. This study reveals the publication patterns and emerging trends of rTMS based on the records published from 2009 to 2018. The insights obtained have reference values for the future research and application of rTMS. Copyright © 2020 Zheng, Dai, Lan and Wang.Hardware architectures composed of resistive cross-point device arrays can provide significant power and speed benefits for deep neural network training workloads using stochastic gradient descent (SGD) and backpropagation (BP) algorithm. Deucravacitinib The training accuracy on this imminent analog hardware, however, strongly depends on the switching characteristics of the cross-point elements. One of the key requirements is that these resistive devices must change conductance in a symmetrical fashion when subjected to positive or negative pulse stimuli. Here, we present a new training algorithm, so-called the "Tiki-Taka" algorithm, that eliminates this stringent symmetry requirement. We show that device asymmetry introduces an unintentional implicit cost term into the SGD algorithm, whereas in the "Tiki-Taka" algorithm a coupled dynamical system simultaneously minimizes the original objective function of the neural network and the unintentional cost term due to device asymmetry in a self-consistent fashion. We tested the validity of this new algorithm on a range of network architectures such as fully connected, convolutional and LSTM networks. Simulation results on these various networks show that the accuracy achieved using the conventional SGD algorithm with symmetric (ideal) device switching characteristics is matched in accuracy achieved using the "Tiki-Taka" algorithm with non-symmetric (non-ideal) device switching characteristics. Moreover, all the operations performed on the arrays are still parallel and therefore the implementation cost of this new algorithm on array architectures is minimal; and it maintains the aforementioned power and speed benefits. These algorithmic improvements are crucial to relax the material specification and to realize technologically viable resistive crossbar arrays that outperform digital accelerators for similar training tasks. Copyright © 2020 Gokmen and Haensch.
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